20 algorithm-development-"Multiple"-"Prof"-"UNIS" Postdoctoral positions at Leibniz in Germany
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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develop research-based recommendations for action for policymakers, business and society. Through our five fields of activity – research, promotion of young researchers, policy advice, participation in
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development. It is one of the world's leading research institutions in its field and offers natural and social scientists from around the world an inspiring environment for excellent interdisciplinary research
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and socially sustainable agriculture – together with society. ZALF is a member of the Leibniz Association and is located in Müncheberg (approx. 35 minutes by regional train from Berlin-Lichtenberg
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, and energy systems into a comprehensive bio-based circular economy. We develop and integrate techniques, processes, and management strategies, effectively converging technologies to intelligently
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development opportunities and annual performance reviews. You are paid according to the collective agreement for the public sector (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), which includes an annual
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institutions, and a research and development provider for numerous companies throughout the world. The INM is a member of the Leibniz Association and has about 250 employees. The INM Research Department Energy
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development. It is one of the world's leading research institutions in its field and offers natural and social scientists from around the world an inspiring environment for excellent interdisciplinary research
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with a focus on transnational terrorism or related topics in international academic journals and with well-established publishers; Development of grant applications and implementation of research
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You